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In many applications where moderate to large datasets are used, plotting relationships between pairs of variables can be problematic. A large number of observations will produce a scatter-plot which is difficu...

The large amounts of data have created a need for new frameworks for processing. The MapReduce model is a framework for processing and generating large-scale datasets with parallel and distributed algorithms. ...

Sentiment analysis becomes ubiquitous for a variety of applications used in marketing, commerce, and public sector. This has been raising a natural interest within the academic research and industry to develop...

Link prediction is one of the most fundamental tasks in statistical network analysis, for which latent feature models have been widely used. As large-scale networks are available in various application domains...

In this era of data science, many software vendors are rushing towards providing better solutions for data management, analytics, validation and security. The government, being one of the most important custom...

Finding orthologous genes among multiple sequenced genomes is a primary step in comparative genomics studies. With the number of sequenced genomes increasing exponentially, comparative genomics becomes more po...

Clustering is a key data mining task. This is the problem of partitioning a set of observations into clusters such that the intra-cluster observations are similar and the inter-cluster observations are dissimi...

Neuromorphic Engineering has emerged as an exciting research area, primarily owing to the paradigm shift from conventional computing architectures to data-driven, cognitive computing. There is a diversity of w...

The massive growth in the scale of data has been observed in recent years being a key factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety of data that require a new hi...

Data-based modeling is becoming practical in predicting outcomes. In the era of big data, two practically conflicting challenges are eminent: (1) the prior knowledge on the subject is largely insufficient; (2)...

Mixed Order Hyper Networks (MOHNs) are a type of neural network in which the interactions between inputs are modelled explicitly by weights that can connect any number of neurons. Such networks have a human re...

To ensure the output quality, current crowdsourcing systems highly rely on redundancy of answers provided by multiple workers with varying expertise, however massive redundancy is very expensive and time-consu...

Real world data analysis problems often require nonlinear methods to get successful prediction. Kernel methods, e.g. Kernelized Principal Component Analysis, are a common way to get nonlinear properties based ...

Human movement such as physical work, exercise and sport activities can be analyzed to determine kinetic (force) and kinematic (motion) characteristics. In the past, proper assessment of force variables requir...

Cyber security is vital to the success of today’s digital economy. The major security threats are coming from within, as opposed to outside forces. Insider threat detection and prediction are important mitigat...

The International Symposium “Advances in Systems Biology in Neurosciences” was held in February 2015 in Geneva. A hundred scientists with a variety of expertise gathered around the theme of human brain complex...

With the explosion of social media sites and proliferation of digital computing devices and Internet access, massive amounts of public data is being generated on a daily basis. Efficient techniques/algorithms ...

Population health management takes into account many determinants of health, including medical care, social and physical environments and related services, genetics, and individual behavior. Many different typ...

This paper discusses the relationship between data science and population-based algorithms, which include swarm intelligence and evolutionary algorithms. We reviewed two categories of literature, which include...

We would like to welcome you to Big Data Analytics, a pioneering multi-disciplinary open access and peer-reviewed journal, which welcomes cutting-edge articles describing biologically-inspired computational, theo...